Ataguba, J. E., Ojo, K. O., & Ichoku, H. E. (2016).
Explaining socio-economic inequalities in immunization coverage in Nigeria.
Health Policy and Planning,
31(9), 1212–1224.
https://doi.org/10.1093/heapol/czw053
Braveman, P., & Gottlieb, L. (2014). The social determinants of health: It’s time to consider the causes of the causes.
Public Health Reports,
129(1_suppl2), 19–31.
https://doi.org/10.1177/00333549141291S206
Broman, K. W. (2015). R/qtlcharts: Interactive graphics for quantitative trait locus mapping.
Genetics,
199, 359–361.
https://doi.org/10.1534/genetics.114.172742
Deb, P., Furceri, D., Jiménez, D., Kothari, S., Ostry, J., & Tawk, N. (2021). The effects of COVID-19 vaccines on economic activity.
SSRN Electronic Journal.
https://doi.org/10.2139/ssrn.4026476
Debeer, Dries, Hothorn, T., & Strobl, C. (2021).
Permimp: Conditional permutation importance. Retrieved from
https://CRAN.R-project.org/package=permimp
Debeer, D., & Strobl, C. (2020). Conditional permutation importance revisited [Journal Article].
BMC Bioinformatics,
21(1), 307.
https://doi.org/10.1186/s12859-020-03622-2
Dewi, C. (2019). Random forest and support vector machine on features selection for regression analysis. International Journal of Innovative Computing, Information & Control: IJICIC, 15, 2027–2037.
E.Gornick, M. (2002). Committee on guidance for designing a national healthcare disparities report. In S. E. K. (Ed.),
2, MEASURING THE EFFECTS OF SOCIOECONOMIC STATUS ON HEALTH CARE. Washington (DC): National Academies Press (US). Retrieved from
https://www.ncbi.nlm.nih.gov/books/NBK221050/
Eilers, R., Krabbe, P. F., & Melker, H. E. de. (2014). Factors affecting the uptake of vaccination by the elderly in western society [Journal Article].
Prev Med,
69, 224–234.
https://doi.org/10.1016/j.ypmed.2014.10.017
Glassman, A. (n.d.). The COVID-19 vaccine rollout was the fastest in global history, but low-income countries were left behind. Retrieved from
https://www.cgdev.org/blog/covid-19-vaccine-rollout-was-fastest-global-history-low-income-countries-were-left-behind
Grolemund, G., & Wickham, H. (2011). Dates and times made easy with
lubridate.
Journal of Statistical Software,
40(3), 1–25. Retrieved from
https://www.jstatsoft.org/v40/i03/
Hannah Ritchie, L. R.-G., Edouard Mathieu, & Roser, M. (2020). Coronavirus pandemic (COVID-19). Our World in Data.
Liaw, A., & Wiener, M. (2002). Classification and regression by randomForest.
R News,
2(3), 18–22. Retrieved from
https://CRAN.R-project.org/doc/Rnews/
Maleva, T. M., Kartseva, M. A., & Korzhuk, S. V. (2021). Socio-demographic determinants of COVID-19 vaccine uptake in russia in the context of mandatory vaccination of employees.
Population and Economics,
5(4), 30–49.
https://doi.org/10.3897/popecon.5.e77832
R Core Team. (2022).
R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. Retrieved from
https://www.R-project.org/
Robinson, D., Hayes, A., & Couch, S. (2022).
Broom: Convert statistical objects into tidy tibbles. Retrieved from
https://CRAN.R-project.org/package=broom
Sievert, C. (2020).
Interactive web-based data visualization with r, plotly, and shiny. Chapman; Hall/CRC. Retrieved from
https://plotly-r.com
Strobl, C., Boulesteix, A.-L., Kneib, T., Augustin, T., & Zeileis, A. (2008). Conditional variable importance for random forests [Journal Article].
BMC Bioinformatics,
9(1), 307.
https://doi.org/10.1186/1471-2105-9-307
Tarantola, D., & Dasgupta, N. (2021). COVID-19 surveillance data: A primer for epidemiology and data science.
American Journal of Public Health,
111(4), 614–619.
https://doi.org/10.2105/AJPH.2020.306088
Wickham, H. (2007). Reshaping data with the
reshape package.
Journal of Statistical Software,
21(12), 1–20. Retrieved from
http://www.jstatsoft.org/v21/i12/
Wickham, H., Averick, M., Bryan, J., Chang, W., McGowan, L. D., François, R., … Yutani, H. (2019). Welcome to the
tidyverse.
Journal of Open Source Software,
4(43), 1686.
https://doi.org/10.21105/joss.01686
Wickham, H., & Bryan, J. (2022).
Readxl: Read excel files. Retrieved from
https://CRAN.R-project.org/package=readxl
Wright, M. N., & Ziegler, A. (2017).
ranger: A fast implementation of random forests for high dimensional data in
C++ and
R.
Journal of Statistical Software,
77(1), 1–17.
https://doi.org/10.18637/jss.v077.i01
Xie, Y., Cheng, J., & Tan, X. (2022).
DT: A wrapper of the JavaScript library ’DataTables’. Retrieved from
https://CRAN.R-project.org/package=DT